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            <h1>Patches Are All You Need? (ConvMixer)</h1>
<p>This is a <a href="https://pytorch.org">PyTorch</a> implementation of the paper <a href="https://arxiv.org/abs/2201.09792">Patches Are All You Need?</a>.</p>
<p><img alt="ConvMixer diagram from the paper" src="conv_mixer.png"></p>
<p>ConvMixer is Similar to <a href="../transformers/mlp_mixer/index.html">MLP-Mixer</a>. MLP-Mixer separates mixing of spatial and channel dimensions, by applying an MLP across spatial dimension and then an MLP across the channel dimension (spatial MLP replaces the <a href="../transformers/vit/index.html">ViT</a> attention and channel MLP is the <a href="../transformers/feed_forward.html">FFN</a> of ViT).</p>
<p>ConvMixer uses a <span ><span class="katex"><span aria-hidden="true" class="katex-html"><span class="base"><span class="strut" style="height:0.72777em;vertical-align:-0.08333em;"></span><span class="mord coloredeq eqa" style=""><span class="mord" style="">1</span><span class="mspace" style="margin-right:0.2222222222222222em;"></span><span class="mbin" style="">×</span><span class="mspace" style="margin-right:0.2222222222222222em;"></span><span class="mord" style="">1</span></span></span></span></span></span> convolution for channel mixing and a depth-wise convolution for spatial mixing. Since it&#x27;s a convolution instead of a full MLP across the space, it mixes only the nearby batches in contrast to ViT or MLP-Mixer. Also, the MLP-mixer uses MLPs of two layers for each mixing and ConvMixer uses a single layer for each mixing.</p>
<p>The paper recommends removing the residual connection across the channel mixing (point-wise convolution) and having only a residual connection over the spatial mixing (depth-wise convolution). They also use <a href="../normalization/batch_norm/index.html">Batch normalization</a> instead of <a href="../normalization/layer_norm/index.html">Layer normalization</a>.</p>
<p>Here&#x27;s <a href="experiment.html">an experiment</a> that trains ConvMixer on CIFAR-10.</p>

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        <div class='code'>
            <div class="highlight"><pre><span class="lineno">36</span><span></span><span class="kn">import</span> <span class="nn">torch</span>
<span class="lineno">37</span><span class="kn">from</span> <span class="nn">torch</span> <span class="kn">import</span> <span class="n">nn</span>
<span class="lineno">38</span>
<span class="lineno">39</span><span class="kn">from</span> <span class="nn">labml_nn.utils</span> <span class="kn">import</span> <span class="n">clone_module_list</span></pre></div>
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    <div class='section' id='section-1'>
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            <p> <a id="ConvMixerLayer"></a></p>
<h2>ConvMixer layer</h2>
<p>This is a single ConvMixer layer. The model will have a series of these.</p>

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        <div class='code'>
            <div class="highlight"><pre><span class="lineno">42</span><span class="k">class</span> <span class="nc">ConvMixerLayer</span><span class="p">(</span><span class="n">nn</span><span class="o">.</span><span class="n">Module</span><span class="p">):</span></pre></div>
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            <ul><li><code  class="highlight"><span></span><span class="n">d_model</span></code>
 is the number of channels in patch embeddings, <span ><span class="katex"><span aria-hidden="true" class="katex-html"><span class="base"><span class="strut" style="height:0.69444em;vertical-align:0em;"></span><span class="mord coloredeq eqd" style=""><span class="mord mathnormal" style="">h</span></span></span></span></span></span> </li>
<li><code  class="highlight"><span></span><span class="n">kernel_size</span></code>
 is the size of the kernel of spatial convolution, <span ><span class="katex"><span aria-hidden="true" class="katex-html"><span class="base"><span class="strut" style="height:0.69444em;vertical-align:0em;"></span><span class="mord mathnormal" style="margin-right:0.03148em;">k</span></span></span></span></span></li></ul>

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        <div class='code'>
            <div class="highlight"><pre><span class="lineno">51</span>    <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">d_model</span><span class="p">:</span> <span class="nb">int</span><span class="p">,</span> <span class="n">kernel_size</span><span class="p">:</span> <span class="nb">int</span><span class="p">):</span></pre></div>
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            <div class="highlight"><pre><span class="lineno">56</span>        <span class="nb">super</span><span class="p">()</span><span class="o">.</span><span class="fm">__init__</span><span class="p">()</span></pre></div>
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            <p>Depth-wise convolution is separate convolution for each channel. We do this with a convolution layer with the number of groups equal to the number of channels. So that each channel is it&#x27;s own group. </p>

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            <div class="highlight"><pre><span class="lineno">60</span>        <span class="bp">self</span><span class="o">.</span><span class="n">depth_wise_conv</span> <span class="o">=</span> <span class="n">nn</span><span class="o">.</span><span class="n">Conv2d</span><span class="p">(</span><span class="n">d_model</span><span class="p">,</span> <span class="n">d_model</span><span class="p">,</span>
<span class="lineno">61</span>                                         <span class="n">kernel_size</span><span class="o">=</span><span class="n">kernel_size</span><span class="p">,</span>
<span class="lineno">62</span>                                         <span class="n">groups</span><span class="o">=</span><span class="n">d_model</span><span class="p">,</span>
<span class="lineno">63</span>                                         <span class="n">padding</span><span class="o">=</span><span class="p">(</span><span class="n">kernel_size</span> <span class="o">-</span> <span class="mi">1</span><span class="p">)</span> <span class="o">//</span> <span class="mi">2</span><span class="p">)</span></pre></div>
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            <p>Activation after depth-wise convolution </p>

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            <div class="highlight"><pre><span class="lineno">65</span>        <span class="bp">self</span><span class="o">.</span><span class="n">act1</span> <span class="o">=</span> <span class="n">nn</span><span class="o">.</span><span class="n">GELU</span><span class="p">()</span></pre></div>
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            <p>Normalization after depth-wise convolution </p>

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            <div class="highlight"><pre><span class="lineno">67</span>        <span class="bp">self</span><span class="o">.</span><span class="n">norm1</span> <span class="o">=</span> <span class="n">nn</span><span class="o">.</span><span class="n">BatchNorm2d</span><span class="p">(</span><span class="n">d_model</span><span class="p">)</span></pre></div>
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            <p>Point-wise convolution is a <span ><span class="katex"><span aria-hidden="true" class="katex-html"><span class="base"><span class="strut" style="height:0.72777em;vertical-align:-0.08333em;"></span><span class="mord coloredeq eqa" style=""><span class="mord" style="">1</span><span class="mspace" style="margin-right:0.2222222222222222em;"></span><span class="mbin" style="">×</span><span class="mspace" style="margin-right:0.2222222222222222em;"></span><span class="mord" style="">1</span></span></span></span></span></span> convolution. i.e. a linear transformation of patch embeddings </p>

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            <div class="highlight"><pre><span class="lineno">71</span>        <span class="bp">self</span><span class="o">.</span><span class="n">point_wise_conv</span> <span class="o">=</span> <span class="n">nn</span><span class="o">.</span><span class="n">Conv2d</span><span class="p">(</span><span class="n">d_model</span><span class="p">,</span> <span class="n">d_model</span><span class="p">,</span> <span class="n">kernel_size</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span></pre></div>
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            <p>Activation after point-wise convolution </p>

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        <div class='code'>
            <div class="highlight"><pre><span class="lineno">73</span>        <span class="bp">self</span><span class="o">.</span><span class="n">act2</span> <span class="o">=</span> <span class="n">nn</span><span class="o">.</span><span class="n">GELU</span><span class="p">()</span></pre></div>
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            <p>Normalization after point-wise convolution </p>

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        <div class='code'>
            <div class="highlight"><pre><span class="lineno">75</span>        <span class="bp">self</span><span class="o">.</span><span class="n">norm2</span> <span class="o">=</span> <span class="n">nn</span><span class="o">.</span><span class="n">BatchNorm2d</span><span class="p">(</span><span class="n">d_model</span><span class="p">)</span></pre></div>
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            <div class="highlight"><pre><span class="lineno">77</span>    <span class="k">def</span> <span class="nf">forward</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">x</span><span class="p">:</span> <span class="n">torch</span><span class="o">.</span><span class="n">Tensor</span><span class="p">):</span></pre></div>
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            <p>For the residual connection around the depth-wise convolution </p>

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            <div class="highlight"><pre><span class="lineno">79</span>        <span class="n">residual</span> <span class="o">=</span> <span class="n">x</span></pre></div>
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            <p>Depth-wise convolution, activation and normalization </p>

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            <div class="highlight"><pre><span class="lineno">82</span>        <span class="n">x</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">depth_wise_conv</span><span class="p">(</span><span class="n">x</span><span class="p">)</span>
<span class="lineno">83</span>        <span class="n">x</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">act1</span><span class="p">(</span><span class="n">x</span><span class="p">)</span>
<span class="lineno">84</span>        <span class="n">x</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">norm1</span><span class="p">(</span><span class="n">x</span><span class="p">)</span></pre></div>
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            <p>Add residual connection </p>

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        <div class='code'>
            <div class="highlight"><pre><span class="lineno">87</span>        <span class="n">x</span> <span class="o">+=</span> <span class="n">residual</span></pre></div>
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            <p>Point-wise convolution, activation and normalization </p>

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            <div class="highlight"><pre><span class="lineno">90</span>        <span class="n">x</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">point_wise_conv</span><span class="p">(</span><span class="n">x</span><span class="p">)</span>
<span class="lineno">91</span>        <span class="n">x</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">act2</span><span class="p">(</span><span class="n">x</span><span class="p">)</span>
<span class="lineno">92</span>        <span class="n">x</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">norm2</span><span class="p">(</span><span class="n">x</span><span class="p">)</span></pre></div>
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            <p> </p>

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            <div class="highlight"><pre><span class="lineno">95</span>        <span class="k">return</span> <span class="n">x</span></pre></div>
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            <p> <a id="PatchEmbeddings"></a></p>
<h2>Get patch embeddings</h2>
<p>This splits the image into patches of size <span ><span class="katex"><span aria-hidden="true" class="katex-html"><span class="base"><span class="strut" style="height:0.7777700000000001em;vertical-align:-0.19444em;"></span><span class="mord coloredeq eqf" style=""><span class="mord mathnormal" style="">p</span></span><span class="mspace" style="margin-right:0.2222222222222222em;"></span><span class="mbin">×</span><span class="mspace" style="margin-right:0.2222222222222222em;"></span></span><span class="base"><span class="strut" style="height:0.625em;vertical-align:-0.19444em;"></span><span class="mord coloredeq eqf" style=""><span class="mord mathnormal" style="">p</span></span></span></span></span></span> and gives an embedding for each patch.</p>

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        <div class='code'>
            <div class="highlight"><pre><span class="lineno">98</span><span class="k">class</span> <span class="nc">PatchEmbeddings</span><span class="p">(</span><span class="n">nn</span><span class="o">.</span><span class="n">Module</span><span class="p">):</span></pre></div>
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            <ul><li><code  class="highlight"><span></span><span class="n">d_model</span></code>
 is the number of channels in patch embeddings <span ><span class="katex"><span aria-hidden="true" class="katex-html"><span class="base"><span class="strut" style="height:0.69444em;vertical-align:0em;"></span><span class="mord coloredeq eqd" style=""><span class="mord mathnormal" style="">h</span></span></span></span></span></span> </li>
<li><code  class="highlight"><span></span><span class="n">patch_size</span></code>
 is the size of the patch, <span ><span class="katex"><span aria-hidden="true" class="katex-html"><span class="base"><span class="strut" style="height:0.625em;vertical-align:-0.19444em;"></span><span class="mord coloredeq eqf" style=""><span class="mord mathnormal" style="">p</span></span></span></span></span></span> </li>
<li><code  class="highlight"><span></span><span class="n">in_channels</span></code>
 is the number of channels in the input image (3 for rgb)</li></ul>

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        <div class='code'>
            <div class="highlight"><pre><span class="lineno">107</span>    <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">d_model</span><span class="p">:</span> <span class="nb">int</span><span class="p">,</span> <span class="n">patch_size</span><span class="p">:</span> <span class="nb">int</span><span class="p">,</span> <span class="n">in_channels</span><span class="p">:</span> <span class="nb">int</span><span class="p">):</span></pre></div>
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            <div class="highlight"><pre><span class="lineno">113</span>        <span class="nb">super</span><span class="p">()</span><span class="o">.</span><span class="fm">__init__</span><span class="p">()</span></pre></div>
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            <p>We create a convolution layer with a kernel size and and stride length equal to patch size. This is equivalent to splitting the image into patches and doing a linear transformation on each patch. </p>

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        <div class='code'>
            <div class="highlight"><pre><span class="lineno">118</span>        <span class="bp">self</span><span class="o">.</span><span class="n">conv</span> <span class="o">=</span> <span class="n">nn</span><span class="o">.</span><span class="n">Conv2d</span><span class="p">(</span><span class="n">in_channels</span><span class="p">,</span> <span class="n">d_model</span><span class="p">,</span> <span class="n">kernel_size</span><span class="o">=</span><span class="n">patch_size</span><span class="p">,</span> <span class="n">stride</span><span class="o">=</span><span class="n">patch_size</span><span class="p">)</span></pre></div>
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            <p>Activation function </p>

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            <div class="highlight"><pre><span class="lineno">120</span>        <span class="bp">self</span><span class="o">.</span><span class="n">act</span> <span class="o">=</span> <span class="n">nn</span><span class="o">.</span><span class="n">GELU</span><span class="p">()</span></pre></div>
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            <p>Batch normalization </p>

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            <div class="highlight"><pre><span class="lineno">122</span>        <span class="bp">self</span><span class="o">.</span><span class="n">norm</span> <span class="o">=</span> <span class="n">nn</span><span class="o">.</span><span class="n">BatchNorm2d</span><span class="p">(</span><span class="n">d_model</span><span class="p">)</span></pre></div>
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    <div class='section' id='section-22'>
        <div class='docs doc-strings'>
            <div class='section-link'>
                <a href='#section-22'>#</a>
            </div>
            <ul><li><code  class="highlight"><span></span><span class="n">x</span></code>
 is the input image of shape <code  class="highlight"><span></span><span class="p">[</span><span class="n">batch_size</span><span class="p">,</span> <span class="n">channels</span><span class="p">,</span> <span class="n">height</span><span class="p">,</span> <span class="n">width</span><span class="p">]</span></code>
</li></ul>

        </div>
        <div class='code'>
            <div class="highlight"><pre><span class="lineno">124</span>    <span class="k">def</span> <span class="nf">forward</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">x</span><span class="p">:</span> <span class="n">torch</span><span class="o">.</span><span class="n">Tensor</span><span class="p">):</span></pre></div>
        </div>
    </div>
    <div class='section' id='section-23'>
        <div class='docs'>
            <div class='section-link'>
                <a href='#section-23'>#</a>
            </div>
            <p>Apply convolution layer </p>

        </div>
        <div class='code'>
            <div class="highlight"><pre><span class="lineno">129</span>        <span class="n">x</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">conv</span><span class="p">(</span><span class="n">x</span><span class="p">)</span></pre></div>
        </div>
    </div>
    <div class='section' id='section-24'>
        <div class='docs'>
            <div class='section-link'>
                <a href='#section-24'>#</a>
            </div>
            <p>Activation and normalization </p>

        </div>
        <div class='code'>
            <div class="highlight"><pre><span class="lineno">131</span>        <span class="n">x</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">act</span><span class="p">(</span><span class="n">x</span><span class="p">)</span>
<span class="lineno">132</span>        <span class="n">x</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">norm</span><span class="p">(</span><span class="n">x</span><span class="p">)</span></pre></div>
        </div>
    </div>
    <div class='section' id='section-25'>
        <div class='docs'>
            <div class='section-link'>
                <a href='#section-25'>#</a>
            </div>
            <p> </p>

        </div>
        <div class='code'>
            <div class="highlight"><pre><span class="lineno">135</span>        <span class="k">return</span> <span class="n">x</span></pre></div>
        </div>
    </div>
    <div class='section' id='section-26'>
        <div class='docs doc-strings'>
            <div class='section-link'>
                <a href='#section-26'>#</a>
            </div>
            <p> <a id="ClassificationHead"></a></p>
<h2>Classification Head</h2>
<p>They do average pooling (taking the mean of all patch embeddings) and a final linear transformation to predict the log-probabilities of the image classes.</p>

        </div>
        <div class='code'>
            <div class="highlight"><pre><span class="lineno">138</span><span class="k">class</span> <span class="nc">ClassificationHead</span><span class="p">(</span><span class="n">nn</span><span class="o">.</span><span class="n">Module</span><span class="p">):</span></pre></div>
        </div>
    </div>
    <div class='section' id='section-27'>
        <div class='docs doc-strings'>
            <div class='section-link'>
                <a href='#section-27'>#</a>
            </div>
            <ul><li><code  class="highlight"><span></span><span class="n">d_model</span></code>
 is the number of channels in patch embeddings, <span ><span class="katex"><span aria-hidden="true" class="katex-html"><span class="base"><span class="strut" style="height:0.69444em;vertical-align:0em;"></span><span class="mord coloredeq eqd" style=""><span class="mord mathnormal" style="">h</span></span></span></span></span></span> </li>
<li><code  class="highlight"><span></span><span class="n">n_classes</span></code>
 is the number of classes in the classification task</li></ul>

        </div>
        <div class='code'>
            <div class="highlight"><pre><span class="lineno">148</span>    <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">d_model</span><span class="p">:</span> <span class="nb">int</span><span class="p">,</span> <span class="n">n_classes</span><span class="p">:</span> <span class="nb">int</span><span class="p">):</span></pre></div>
        </div>
    </div>
    <div class='section' id='section-28'>
        <div class='docs'>
            <div class='section-link'>
                <a href='#section-28'>#</a>
            </div>
            
        </div>
        <div class='code'>
            <div class="highlight"><pre><span class="lineno">153</span>        <span class="nb">super</span><span class="p">()</span><span class="o">.</span><span class="fm">__init__</span><span class="p">()</span></pre></div>
        </div>
    </div>
    <div class='section' id='section-29'>
        <div class='docs'>
            <div class='section-link'>
                <a href='#section-29'>#</a>
            </div>
            <p>Average Pool </p>

        </div>
        <div class='code'>
            <div class="highlight"><pre><span class="lineno">155</span>        <span class="bp">self</span><span class="o">.</span><span class="n">pool</span> <span class="o">=</span> <span class="n">nn</span><span class="o">.</span><span class="n">AdaptiveAvgPool2d</span><span class="p">((</span><span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">))</span></pre></div>
        </div>
    </div>
    <div class='section' id='section-30'>
        <div class='docs'>
            <div class='section-link'>
                <a href='#section-30'>#</a>
            </div>
            <p>Linear layer </p>

        </div>
        <div class='code'>
            <div class="highlight"><pre><span class="lineno">157</span>        <span class="bp">self</span><span class="o">.</span><span class="n">linear</span> <span class="o">=</span> <span class="n">nn</span><span class="o">.</span><span class="n">Linear</span><span class="p">(</span><span class="n">d_model</span><span class="p">,</span> <span class="n">n_classes</span><span class="p">)</span></pre></div>
        </div>
    </div>
    <div class='section' id='section-31'>
        <div class='docs'>
            <div class='section-link'>
                <a href='#section-31'>#</a>
            </div>
            
        </div>
        <div class='code'>
            <div class="highlight"><pre><span class="lineno">159</span>    <span class="k">def</span> <span class="nf">forward</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">x</span><span class="p">:</span> <span class="n">torch</span><span class="o">.</span><span class="n">Tensor</span><span class="p">):</span></pre></div>
        </div>
    </div>
    <div class='section' id='section-32'>
        <div class='docs'>
            <div class='section-link'>
                <a href='#section-32'>#</a>
            </div>
            <p>Average pooling </p>

        </div>
        <div class='code'>
            <div class="highlight"><pre><span class="lineno">161</span>        <span class="n">x</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">pool</span><span class="p">(</span><span class="n">x</span><span class="p">)</span></pre></div>
        </div>
    </div>
    <div class='section' id='section-33'>
        <div class='docs'>
            <div class='section-link'>
                <a href='#section-33'>#</a>
            </div>
            <p>Get the embedding, <code  class="highlight"><span></span><span class="n">x</span></code>
 will have shape <code  class="highlight"><span></span><span class="p">[</span><span class="n">batch_size</span><span class="p">,</span> <span class="n">d_model</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">]</span></code>
 </p>

        </div>
        <div class='code'>
            <div class="highlight"><pre><span class="lineno">163</span>        <span class="n">x</span> <span class="o">=</span> <span class="n">x</span><span class="p">[:,</span> <span class="p">:,</span> <span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">]</span></pre></div>
        </div>
    </div>
    <div class='section' id='section-34'>
        <div class='docs'>
            <div class='section-link'>
                <a href='#section-34'>#</a>
            </div>
            <p>Linear layer </p>

        </div>
        <div class='code'>
            <div class="highlight"><pre><span class="lineno">165</span>        <span class="n">x</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">linear</span><span class="p">(</span><span class="n">x</span><span class="p">)</span></pre></div>
        </div>
    </div>
    <div class='section' id='section-35'>
        <div class='docs'>
            <div class='section-link'>
                <a href='#section-35'>#</a>
            </div>
            <p> </p>

        </div>
        <div class='code'>
            <div class="highlight"><pre><span class="lineno">168</span>        <span class="k">return</span> <span class="n">x</span></pre></div>
        </div>
    </div>
    <div class='section' id='section-36'>
        <div class='docs doc-strings'>
            <div class='section-link'>
                <a href='#section-36'>#</a>
            </div>
            <h2>ConvMixer</h2>
<p>This combines the patch embeddings block, a number of ConvMixer layers and a classification head.</p>

        </div>
        <div class='code'>
            <div class="highlight"><pre><span class="lineno">171</span><span class="k">class</span> <span class="nc">ConvMixer</span><span class="p">(</span><span class="n">nn</span><span class="o">.</span><span class="n">Module</span><span class="p">):</span></pre></div>
        </div>
    </div>
    <div class='section' id='section-37'>
        <div class='docs doc-strings'>
            <div class='section-link'>
                <a href='#section-37'>#</a>
            </div>
            <ul><li><code  class="highlight"><span></span><span class="n">conv_mixer_layer</span></code>
 is a copy of a single <a href="#ConvMixerLayer">ConvMixer layer</a>.  We make copies of it to make ConvMixer with <code  class="highlight"><span></span><span class="n">n_layers</span></code>
. </li>
<li><code  class="highlight"><span></span><span class="n">n_layers</span></code>
 is the number of ConvMixer layers (or depth), <span ><span class="katex"><span aria-hidden="true" class="katex-html"><span class="base"><span class="strut" style="height:0.69444em;vertical-align:0em;"></span><span class="mord mathnormal">d</span></span></span></span></span>. </li>
<li><code  class="highlight"><span></span><span class="n">patch_emb</span></code>
 is the <a href="#PatchEmbeddings">patch embeddings layer</a>. </li>
<li><code  class="highlight"><span></span><span class="n">classification</span></code>
 is the <a href="#ClassificationHead">classification head</a>.</li></ul>

        </div>
        <div class='code'>
            <div class="highlight"><pre><span class="lineno">178</span>    <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">conv_mixer_layer</span><span class="p">:</span> <span class="n">ConvMixerLayer</span><span class="p">,</span> <span class="n">n_layers</span><span class="p">:</span> <span class="nb">int</span><span class="p">,</span>
<span class="lineno">179</span>                 <span class="n">patch_emb</span><span class="p">:</span> <span class="n">PatchEmbeddings</span><span class="p">,</span>
<span class="lineno">180</span>                 <span class="n">classification</span><span class="p">:</span> <span class="n">ClassificationHead</span><span class="p">):</span></pre></div>
        </div>
    </div>
    <div class='section' id='section-38'>
        <div class='docs'>
            <div class='section-link'>
                <a href='#section-38'>#</a>
            </div>
            
        </div>
        <div class='code'>
            <div class="highlight"><pre><span class="lineno">188</span>        <span class="nb">super</span><span class="p">()</span><span class="o">.</span><span class="fm">__init__</span><span class="p">()</span></pre></div>
        </div>
    </div>
    <div class='section' id='section-39'>
        <div class='docs'>
            <div class='section-link'>
                <a href='#section-39'>#</a>
            </div>
            <p>Patch embeddings </p>

        </div>
        <div class='code'>
            <div class="highlight"><pre><span class="lineno">190</span>        <span class="bp">self</span><span class="o">.</span><span class="n">patch_emb</span> <span class="o">=</span> <span class="n">patch_emb</span></pre></div>
        </div>
    </div>
    <div class='section' id='section-40'>
        <div class='docs'>
            <div class='section-link'>
                <a href='#section-40'>#</a>
            </div>
            <p>Classification head </p>

        </div>
        <div class='code'>
            <div class="highlight"><pre><span class="lineno">192</span>        <span class="bp">self</span><span class="o">.</span><span class="n">classification</span> <span class="o">=</span> <span class="n">classification</span></pre></div>
        </div>
    </div>
    <div class='section' id='section-41'>
        <div class='docs'>
            <div class='section-link'>
                <a href='#section-41'>#</a>
            </div>
            <p>Make copies of the <a href="#ConvMixerLayer">ConvMixer layer</a> </p>

        </div>
        <div class='code'>
            <div class="highlight"><pre><span class="lineno">194</span>        <span class="bp">self</span><span class="o">.</span><span class="n">conv_mixer_layers</span> <span class="o">=</span> <span class="n">clone_module_list</span><span class="p">(</span><span class="n">conv_mixer_layer</span><span class="p">,</span> <span class="n">n_layers</span><span class="p">)</span></pre></div>
        </div>
    </div>
    <div class='section' id='section-42'>
        <div class='docs doc-strings'>
            <div class='section-link'>
                <a href='#section-42'>#</a>
            </div>
            <ul><li><code  class="highlight"><span></span><span class="n">x</span></code>
 is the input image of shape <code  class="highlight"><span></span><span class="p">[</span><span class="n">batch_size</span><span class="p">,</span> <span class="n">channels</span><span class="p">,</span> <span class="n">height</span><span class="p">,</span> <span class="n">width</span><span class="p">]</span></code>
</li></ul>

        </div>
        <div class='code'>
            <div class="highlight"><pre><span class="lineno">196</span>    <span class="k">def</span> <span class="nf">forward</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">x</span><span class="p">:</span> <span class="n">torch</span><span class="o">.</span><span class="n">Tensor</span><span class="p">):</span></pre></div>
        </div>
    </div>
    <div class='section' id='section-43'>
        <div class='docs'>
            <div class='section-link'>
                <a href='#section-43'>#</a>
            </div>
            <p>Get patch embeddings. This gives a tensor of shape <code  class="highlight"><span></span><span class="p">[</span><span class="n">batch_size</span><span class="p">,</span> <span class="n">d_model</span><span class="p">,</span> <span class="n">height</span> <span class="o">/</span> <span class="n">patch_size</span><span class="p">,</span> <span class="n">width</span> <span class="o">/</span> <span class="n">patch_size</span><span class="p">]</span></code>
. </p>

        </div>
        <div class='code'>
            <div class="highlight"><pre><span class="lineno">201</span>        <span class="n">x</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">patch_emb</span><span class="p">(</span><span class="n">x</span><span class="p">)</span></pre></div>
        </div>
    </div>
    <div class='section' id='section-44'>
        <div class='docs'>
            <div class='section-link'>
                <a href='#section-44'>#</a>
            </div>
            <p>Pass through <a href="#ConvMixerLayer">ConvMixer layers</a> </p>

        </div>
        <div class='code'>
            <div class="highlight"><pre><span class="lineno">204</span>        <span class="k">for</span> <span class="n">layer</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">conv_mixer_layers</span><span class="p">:</span>
<span class="lineno">205</span>            <span class="n">x</span> <span class="o">=</span> <span class="n">layer</span><span class="p">(</span><span class="n">x</span><span class="p">)</span></pre></div>
        </div>
    </div>
    <div class='section' id='section-45'>
        <div class='docs'>
            <div class='section-link'>
                <a href='#section-45'>#</a>
            </div>
            <p>Classification head, to get logits </p>

        </div>
        <div class='code'>
            <div class="highlight"><pre><span class="lineno">208</span>        <span class="n">x</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">classification</span><span class="p">(</span><span class="n">x</span><span class="p">)</span></pre></div>
        </div>
    </div>
    <div class='section' id='section-46'>
        <div class='docs'>
            <div class='section-link'>
                <a href='#section-46'>#</a>
            </div>
            <p> </p>

        </div>
        <div class='code'>
            <div class="highlight"><pre><span class="lineno">211</span>        <span class="k">return</span> <span class="n">x</span></pre></div>
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